ISSN
| 1941-7020 |
DDC
| 004 |
Nhan đề
| A STING algorithm and multi-dimensional vectors used for english sentiment classification in a distributed system / Vo Ngoc Phu, Vo Thi Ngoc Tran |
Thông tin xuất bản
| 2017 |
Thông tin xuất bản
| Science Publications |
Mô tả vật lý
| 19 p. ; |
Tóm tắt
| Sentiment classification is significant in everyday life, such as in political activities, commodity production and commercial activities. Finding a fast, highly accurate solution to classify emotion has been a challenge for scientists. In this research, we have proposed a new model for Big Data sentiment classification in the parallel network environment - a Cloudera system with Hadoop Map (M) and Hadoop Reduce (R). Our new model has used a Statistical Information Grid Algorithm (STING) with multi-dimensional vector and 2,000,000 English documents of our English training data set for English document-level sentiment classification. Our new model can classify sentiment of millions of English documents based on many English documents in the parallel network environment. However, we tested our new model on our testing data set (including 1,000,000 English reviews, 500,000 positive and 500,000 negative) and achieved 83.92% accuracy. |
Từ khóa tự do
| English Document Opinion Mining |
Từ khóa tự do
| English Sentiment Classification |
Từ khóa tự do
| Opinion Mining |
Từ khóa tự do
| Sentiment Classification |
Từ khóa tự do
| Distributed System |
Từ khóa tự do
| Parallel System |
Từ khóa tự do
| STING |
Khoa
| Khoa Công nghệ Thông tin |
Tác giả(bs) CN
| Vo, Thi Ngoc Tran |
Tác giả(bs) CN
| Vo, Ngoc Phu |
Nguồn trích
| .
Số: Vol. 11, Issue 1, P. 19-37, , |
Nguồn trích
| American Journal of Engineering and Applied Sciences. , , |
Địa chỉ
| Thư Viện Đại học Nguyễn Tất Thành |
Tệp tin điện tử
| https://thescipub.com/abstract/10.3844/ajeassp.2018.19.37 |
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245 | |aA STING algorithm and multi-dimensional vectors used for english sentiment classification in a distributed system / |cVo Ngoc Phu, Vo Thi Ngoc Tran |
---|
260 | |c2017 |
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260 | |bScience Publications |
---|
300 | |a19 p. ; |
---|
520 | |aSentiment classification is significant in everyday life, such as in political activities, commodity production and commercial activities. Finding a fast, highly accurate solution to classify emotion has been a challenge for scientists. In this research, we have proposed a new model for Big Data sentiment classification in the parallel network environment - a Cloudera system with Hadoop Map (M) and Hadoop Reduce (R). Our new model has used a Statistical Information Grid Algorithm (STING) with multi-dimensional vector and 2,000,000 English documents of our English training data set for English document-level sentiment classification. Our new model can classify sentiment of millions of English documents based on many English documents in the parallel network environment. However, we tested our new model on our testing data set (including 1,000,000 English reviews, 500,000 positive and 500,000 negative) and achieved 83.92% accuracy. |
---|
653 | |aEnglish Document Opinion Mining |
---|
653 | |aEnglish Sentiment Classification |
---|
653 | |aOpinion Mining |
---|
653 | |aSentiment Classification |
---|
653 | |aDistributed System |
---|
653 | |aParallel System |
---|
653 | |aSTING |
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690 | |aKhoa Công nghệ Thông tin |
---|
700 | |aVo, Thi Ngoc Tran |
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700 | |aVo, Ngoc Phu |
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773 | |gVol. 11, Issue 1, P. 19-37 |
---|
773 | |tAmerican Journal of Engineering and Applied Sciences |
---|
852 | |aThư Viện Đại học Nguyễn Tất Thành |
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856 | |uhttps://thescipub.com/abstract/10.3844/ajeassp.2018.19.37 |
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890 | |c1|a0|b0|d1 |
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